Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals

Bassam Moslem,Catherine Marque,Mohamad Khalil,Mohamad Diab

doi:10.1186/1687-6180-2012-167

Abstract

Multisensor data fusion is a powerful solution for solving difficult pattern recognition problems such as the classification of bioelectrical signals. It is the process of combining information from different sensors to provide a more stable and more robust classification decisions. We combine here data fusion with multiresolution analysis based on the wavelet packet transform (WPT) in order to classify real uterine electromyogram (EMG) signals recorded by 16 electrodes. Herein, the data fusion is done at the decision level by using a weighted majority voting (WMV) rule. On the other hand, the WPT is used to achieve significant enhancement in the classification performance of each channel by improving the discrimination power of the selected feature. We show that the proposed approach tested on our recorded data can improve the recognition accuracy in labor prediction and has a competitive and promising performance.

Highlights

Bioelectrical signals express the electrical functionality of different organs in the human body
As our ultimate goal is to improve the classification accuracy of uterine EMG signals in order to help detect preterm labor, we find these results to be very useful
The classification of uterine EMG signals recorded by using multiple sensors was addressed

Summary

Introduction

Bioelectrical signals express the electrical functionality of different organs in the human body. Studies have shown that uterine EMG can provide valuable information about the function aspects of the uterine contractility [1,2]. It is potentially the best predictor of preterm labor and of great value for the diagnosis of preterm delivery [3]. Lu et al [5] presented a classification method based on the wavelet packet decomposition and a multilayer Perceptron (MLP) to differ between term and preterm data. Their study included 11 preterm and 28 term signals They reported a classification accuracy of 64.1%. The results showed first that it was possible to detect a risk of preterm labor as early as 27 weeks of gestation with a classification accuracy of 87%

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